(untitled) (Publicaciones en Ciencias y Tecnología, 2015)
In this work we dealt with matrix completion problem. This problem arises in different fields, for example, systems and control theory, image processing and collaborative filtering. Given a probabilistic matrix factorization model, we present an approach based on Bayesian statistics and a stochastic expectation maximization algorithm to retrieve an array of data from a sample of its inputs. The proposed method does not require regularization parameters and estimates the rank of the matrix, in contrast to the BPMF method. The results show that the proposed method outperforms the rank of the matrix comparing to an augmented lagrangian algorithm and it is more efficient than the BPMF method.